An optimized fuzzy method for finding actions

Abstract

Most of machine learning and data mining techniques only focus on finding patterns satisfying technical interestingness measures. They do not consider business interestingness measures in the mining process, so most of mined patterns may not be usable in corresponding domain. Moreover, the techniques do not interpret the patterns in order that business people know what actions to take to support their business decisions. They require a considerable amount of extra work by domain experts to interpret the patterns. Actionable Knowledge Discovery techniques deliver technical and business significant patterns and support automatically converting patterns into deliverables in business-friendly and operable forms such as actions. In this paper, we incorporate fuzzy set theory and attribute flexibility for the enhancement fuzzy cost-effective action mining algorithm (F-CEAMA). We present an algorithm that suggests actions regarding to their practical applicability. The algorithm takes into account attribute flexibility and fuzzy cost of actions and attempts to maximize the fuzzy net profit. The contribution of the work is in taking the output from fuzzy decision tree, using flexibility of its attributes, and producing novel and actionable knowledge through automatic fuzzy post processing. The performance of proposed algorithm is compared to F-CEAMA using several real-life datasets taken from the UCI Machine Learning Repository. Experimental results show that the proposed algorithm outperforms F-CEAMA not only in finding more actions but also in finding actions with more fuzzy net profit.